NLP in Healthcare: Revolutionizing Patient Care & Operations

· Thomas Wood
NLP in Healthcare: Revolutionizing Patient Care & Operations

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How NLP Enhances Healthcare Delivery and Operations (+ 8 NLP Strategies and 8 Applications)

Natural Language Processing (NLP) in healthcare is just what the industry needs. Why? It lets computers understand how we speak and write. This is so important and industry-changing because often there is an overwhelming amount of patient information and notes that need quick sorting. NLP in healthcare helps improve patient care and makes healthcare work better.

The NLP market in healthcare is expected to grow big - from USD 2.7 billion in 2023 to USD 11.8 billion by 2028. This shows how much people trust NLP to change healthcare for the better​. And for good reasons…

Industry experts suggest that it promises to “revolutionise Healthcare & Life sciences, simplify data analysis, and ultimately transform healthcare and services for improved outcomes”.

It’s also interesting to imagine a world where computers can instantly understand all medical records. How would that change how we look after patients?

Today, we’ll talk about how NLP is making a difference in healthcare. We’ll look at 8 NLP strategies and 8 real-world examples that show its power – and might give you an idea of how NLP could be useful to you or your colleagues.

What is NLP & What Is Its Role in Healthcare?

Imagine Natural Language Processing (NLP) as a bridge. Not the kind you drive over, but one that connects human chat and computer smarts. NLP sifts through large amounts of unstructured data — think electronic health records (EHRs), notes from the doctor, or even feedback from patients.

At its heart, NLP teaches computers to grasp and make sense of our language in ways that are both clever and practical.

Why does this matter for healthcare professionals? It means they can quickly find the info they need from a sea of data. This contributes to patients getting top-notch care and the wheels of healthcare turning smoothly. It makes it possible for doctors, nurses, and other healthcare professionals to do what they do best: help patients.

The Top 8 NLP Strategies That Make a Difference in Healthcare

Several NLP techniques are particularly beneficial in healthcare settings. We’ll introduce you to the seven NLP strategies that are making the biggest difference in healthcare right now:

  • Text Classification: Imagine a tool that sorts through the masses of healthcare data and puts them into neat, predefined categories. Say a patient drops an email – it can automatically find its way into bins labelled appointment queries, billing concerns, or prescription refill requests.
  • Sentiment Analysis: This tool takes a look at the tone and sentiment of patient feedback or notes. Ever wondered how satisfied your patients are? Sentiment analysis can help you find out. This feedback can help healthcare providers figure out which areas of their services they need to improve.
  • Named Entity Recognition (NER): Picture a sophisticated system that spots and tags important bits in medical documents (like drug names, dosages, and conditions). That’s’ what NER does. Imagine the ease for medical staff when they are reviewing patient histories, with current medications highlighted and ready to review.
  • Optical Character Recognition (OCR): Turning handwritten scribbles and printed pages into digital text is what OCR is all about. This transformation digitises patient records from the past and improves data access. Digital archives mean quicker data retrieval and analysis.
  • Topic Modelling: This method is like a detective who is discovering hidden themes in huge text collections. It’s perfect for spotting trends in diseases, treatments, and patient sentiments. Analysing patient forum discussions could reveal shared experiences or concerns about treatments, which would then offer valuable insights for patient care strategies.
  • Named Entity Linking (NEL): Building on NER, NEL doesn’t just identify entities but connects them to relevant info in a knowledge base. Think of it like this: it can diagnose a condition in patient notes and, with a click, help you access the latest guidelines or research on it.
  • Language Modelling: This method is all about guessing what word comes next in a sentence, using the ones before it as clues. It’s used for creating clinical documents and simulating conversations with patients. Imagine having a conversation where the other party not only understands what you’re saying but also anticipates what you’re about to say next. That’s what language modelling is good at. It makes it simpler to draft up clinical notes and interactive simulations that feel natural.

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  • Speech Recognition: This is where the magic of turning spoken words into written text happens. Speech recognition revolutionises patient records and telehealth chats. It’s making healthcare more reachable by allowing doctors and patients to communicate more freely, without the barrier of typing or writing by hand. With speech recognition in healthcare, everything from a quick consultation over the phone to updating a patient’s health record can be done more efficiently, so no detail is missed.

These NLP techniques help healthcare professionals move through information overload. The result? Better patient care, sharper research, and an all-around smoother operation.

The Big Impact of NLP on Healthcare Operations

NLP significantly improves the healthcare industry in numerous ways. Let’s take a look at the three primary use cases where NLP has made impactful contributions: simplifying healthcare records, matching patients with trials, and helping doctors make better decisions.

Simplifying Healthcare Records

What’s the backbone of healthcare? Clinical documentation. Records that track patient history, diagnoses, treatment plans, and outcomes are the foundation of good patient care and healthcare operations. NLP transforms this realm by effortlessly turning spoken or handwritten notes into structured, digital formats. The result? Time-saving, improved accuracy, and better accessibility.

Matching Patients with Trials

The hunt for the right clinical trial candidates can be like finding a needle in a haystack. Enter NLP. It automates the painstaking process of sifting through endless patient data to find matches for specific trial criteria. This not only hastens the selection process but also brings new treatment opportunities to patients' doorsteps.

Helping Doctors' Decisions

NLP helps doctors by giving them the important information they need from different places like medical books, electronic health records (EHRs), and notes about patients. This large amount of information helps doctors make good choices about what issue a patient is facing and how to treat them. NLP looks through masses of data to find helpful tips and patterns that aren’t easy to see at first. This can simplify complex health issues and help healthcare professionals find the best treatments.

8 Real-Life NLP Applications in Healthcare

Natural Language Processing doesn’t just excel in “general” help. It also fits into specialized roles that boost patient care, data handling, and operational smoothness. Let’s walk through eight NLP applications in healthcare that are changing the game:

1. Clinical Named Entity Recognition (NER) General Model

This tool scans text to find important medical terms, such as diseases, treatments, and medications. It’s very useful for pulling out important information from large amounts of data. It can help with research and keep healthcare providers updated on patient care and treatment options.

2. Clinical Entity Resolver

This tool helps match clinical notes with medical codes, like ICD-10 codes. It works like a translator that makes sure billing is accurate, reports are precise, and health records can be easily shared across different systems.

3. Clinical Relation Extraction Model

This goes a step further to find relationships between medical terms in texts, such as how a drug affects a disease or the link between symptoms and diseases. It provides valuable information for clinical decision-making and gives healthcare providers a fuller picture to base their decisions on.

4. Clinical Assertion Model

This NLP tool reviews clinical notes to determine if a condition or symptom is present, absent, or possible. It acts like a magnifying glass over patient records and provides clear insights for accurate diagnoses and treatment plans.

5. Patient Sentiment Analysis

This analyses patient feedback and notes to understand how patients feel about their treatments and healthcare services. It helps healthcare providers understand patient satisfaction and concerns. It also allows them to improve the patient experience by addressing individual emotional and psychological needs.

6. Clinical Deidentification Model

Because patient data is sensitive, this tool removes personal information from clinical documents to comply with privacy laws like HIPAA. It guarantees patient confidentiality while allowing data to be used for research and analysis.

7. Automated Patient Instruction Generation

This uses NLP to turn medical terms into easy-to-understand instructions for patients. It automates the creation of clear guidelines for medications, post-treatment care, and lifestyle changes. It helps make sure that patients understand their treatment plans and can follow them correctly.

8. Financial Contract Named Entity Recognition

This application focuses on the administrative side by scanning financial and contractual documents. It identifies and organizes key information like service codes, payment terms, and involved parties. It helps streamline billing and contract management and makes healthcare operations more efficient.

NLP’s Impact on Patient Care and Healthcare Operations

NLP is bringing fresh perspectives to patient care and the seamless operation of healthcare services. With its ability to decode and make sense of human language, NLP opens doors to sharper diagnoses, tailored treatment plans, and a better grasp of what patients truly need.

For anyone who is working in healthcare, incorporating NLP means turning a sea of unstructured data — from clinical observations to patient feedback — into valuable insights quickly and efficiently. This not only boosts patient care by giving health professionals a fuller picture but also simplifies healthcare operations in general. Imagine tools that automate all the mundane tasks (from data entry and managing patient records) as well as lend a hand in difficult clinical decisions.

At Fast Data Science, this is our strength. We tailor NLP solutions to meet the unique needs of healthcare providers and professionals like you.

If you’re interested in learning more, our article on The Brave New World of AI in Pharmaceuticals gives a good picture of our journey through the exciting possibilities of AI and NLP in reshaping pharmaceutical research and healthcare offerings.

Why Choose Fast Data Science for NLP Solutions?

Our dedicated team at Fast Data Science, led by Thomas Wood, brings a wealth of experience and a proven track record in using NLP in healthcare to address the specific challenges faced by the industry. Here’s what we do:

  • Tailored NLP Solutions: We recognise the individuality of each healthcare provider and research entity. That’s why we craft NLP solutions that meld with your existing systems flawlessly. Our goal? To deliver solutions that aren’t just efficient but hit the mark on your specific objectives. If you’d like to see an example, our work in AI for healthcare shows our commitment to creating tailored NLP applications that enhance patient care and operational efficiency.
  • A to Z NLP Services: No matter if it’s drawing out key insights from unstructured data or implementing machine learning models that transform patient care – our services span the full range of NLP’s potential in healthcare. From refining clinical documentation to enhancing clinical trial matches, such as our partnership in the Boehringer Ingelheim Clinical Trials Analysis, we use our ability to leverage NLP in meaningful ways.
  • Making a Real Difference: Our work, like the Clinical Trial Risk Tool, is proof of our ability to turn NLP technology into real-world advantages for the healthcare sector. By simplifying complex tasks and unlocking the secrets in clinical data, we equip healthcare providers to offer superior care and advance medical research.
  • Focused on Collaboration: The praise from our clients speaks volumes about our commitment to excellence and teamwork. When you choose to work with Fast Data Science, you partner with a team that’s attentive, adaptable and focused on providing genuine value. Our successful partnerships, such as with the NHS in predicting employee attrition are evidence of our skills in delivering impactful healthcare solutions.

Better Care Through NLP in Healthcare

The arrival of NLP brings a big change to healthcare – a change that’s needed to keep up with how healthcare delivery and research are evolving. NLP helps us handle large amounts of data efficiently, improves patient care and makes healthcare operations better.

Here at Fast Data Science, we’re leading this shift by creating NLP solutions that fit well with what healthcare providers already use. Our goal is to make healthcare operations smoother and take patient care to a higher level.

With NLP, we’re focused on a bigger goal: we’re reminded that “healthcare is not just about adding years to life but about adding life to years.” This philosophy is at the heart of our mission, where using NLP goes beyond technical achievements to truly better the lives of patients and make things easier for healthcare professionals.

Our experience spans across various domains, including pharmaceuticals and academic research. We’re confident that we can help you too!

Curious about how our NLP services can transform your healthcare operations and patient care strategies? We’re all ears and ready to discuss how we can tailor our services to fit your needs.

Reach out to us or call us at +44 20 3488 5740 to begin tapping into the immense capabilities of NLP in healthcare.

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